# Buildsheet autogenerated by ravenadm tool -- Do not edit.

NAMEBASE=		R-lava
VERSION=		1.8.0
KEYWORDS=		cran
VARIANTS=		standard
SDESC[standard]=	Latent Variable Models
HOMEPAGE=		https://kkholst.github.io/lava/
CONTACT=		CRAN_Automaton[cran@ironwolf.systems]

DOWNLOAD_GROUPS=	main
SITES[main]=		CRAN/src/contrib
			https://loki.dragonflybsd.org/cranfiles/
DISTFILE[1]=		lava_1.8.0.tar.gz:main
DIST_SUBDIR=		CRAN
DF_INDEX=		1
SPKGS[standard]=	single

OPTIONS_AVAILABLE=	none
OPTIONS_STANDARD=	none

BUILDRUN_DEPENDS=	R-cli:single:standard
			R-future.apply:single:standard
			R-numDeriv:single:standard
			R-progressr:single:standard
			R-SQUAREM:single:standard

USES=			cran gmake

DISTNAME=		lava

GENERATED=		yes

INSTALL_REQ_TOOLCHAIN=	yes

[FILE:809:descriptions/desc.single]
lava: Latent Variable Models

A general implementation of Structural Equation Models with latent
variables (MLE, 2SLS, and composite likelihood estimators) with both
continuous, censored, and ordinal outcomes (Holst and Budtz-Joergensen
(2013) &lt;<a href="https://doi.org/10.1007%2Fs00180-012-0344-y"
target="_top">doi:10.1007/s00180-012-0344-y</a>&gt;). Mixture latent
variable models and non-linear latent variable models (Holst and
Budtz-Joergensen (2020) &lt;<a
href="https://doi.org/10.1093%2Fbiostatistics%2Fkxy082"
target="_top">doi:10.1093/biostatistics/kxy082</a>&gt;). The package also
provides methods for graph exploration (d-separation, back-door criterion),
simulation of general non-linear latent variable models, and estimation of
influence functions for a broad range of statistical models.


[FILE:101:distinfo]
8db996eeca012c58736f2d3b97f569c03e9361e20f31513c090a9386eb87e87f      1169695 CRAN/lava_1.8.0.tar.gz